Report on eutrophication studies in the U.S.S.R.
4
Citation
1
Reference
10
Related Paper
Citation Trend
Keywords:
Bloom
Abstract Eutrophication and warming are changing the functioning of lake ecosystems, and their impacts on lake carbon dioxide (CO 2 ) variability have received increasing attention. However, how eutrophication and warming change lakes' carbon cycle has not been determined. Here, the surface partial pressure of CO 2 ( p CO 2 ) and CO 2 flux in Lake Taihu, a large and eutrophic lake in eastern China, was investigated based on monthly samplings over a 24‐yr period (1992–2015), during which the lake experienced profound anthropogenic and climate changes. The results showed that eutrophication caused by nutrient enrichment plays a role in three aspects: (1) nutrient concentrations controlled the CO 2 variability on decadal scales; (2) peak p CO 2 and CO 2 fluxes occurred in river mouths due to large external nutrient loading inputs; and (3) eutrophication effects on CO 2 varied among subzones, which was linked to external inputs and in‐lake primary production. Meanwhile, temperature controls the seasonal variation in CO 2 by stimulating primary production, leading to significantly lower p CO 2 and CO 2 fluxes in warm seasons with algal blooms. Further analysis suggested that temperature effects varied spatially and temporally, high nutrient loading may confound the temperature effects via stimulating CO 2 production. To our knowledge, this study presents the longest field measurements (24 yr) of CO 2 from such large and ice‐free freshwater lakes with monthly surveys, which may provide a powerful example to demonstrate that eutrophication and warming can shape CO 2 variability from a temporal perspective. Future studies should focus on the interactive warming and eutrophication effects to accurately predict future CO 2 emission.
Lake ecosystem
Cite
Citations (58)
In several countries, the public health and fishery industries have suffered from harmful algal blooms (HABs) that have escalated to become a global issue. Though computational modeling offers an effective means to understand and mitigate the adverse effects of HABs, it is challenging to design models that adequately reflect the complexity of HAB dynamics. This paper presents a method involving the application of deep learning to an ocean model for simulating blooms of Alexandrium catenella . The classification and regression convolutional neural network (CNN) models are used for simulating the blooms. The classification CNN determines the bloom initiation while the regression CNN estimates the bloom density. GoogleNet and Resnet 101 are identified as the best structures for the classification and regression CNNs, respectively. The corresponding accuracy and root means square error values are determined as 96.8% and 1.20 [log(cells L –1 )], respectively. The results obtained in this study reveal the simulated distribution to follow the Alexandrium catenella bloom. Moreover, Grad-CAM identifies that the salinity and temperature contributed to the initiation of the bloom whereas NH 4 -N influenced the growth of the bloom.
Bloom
Red tide
Cite
Citations (19)
Widespread coastal eutrophication is known to increase the prevalence of harmful algal blooms (HABs). Increased HABs have also been linked to climate change, with ocean warming predicted to lead to increased prevalence and earlier timing of HABs. Testing the predictions of warming to HABs is difficult due to the lack of long-term observations across spatial scales. Here, we use a 45 year (1970-2015) record of the occurrence and duration of HABs along Chinese coast to show that the HAB frequency has increased at a rate of 40 ± 4% decade-1, with earlier timing by 5.50 ± 1.78 days decade-1. The increasing frequency of blooms varied with latitude and is significantly correlated with warming at an average rate of 0.17 ± 0.03 °C decade-1, with the positive relationship being strongest in more eutrophic provinces. HAB frequency increased with elevated dissolved inorganic nutrient concentration, but this increase was amplified further with warming. Warming and eutrophication showed additive roles in triggering HABs. Swift action to mitigate eutrophication is essential to avoid a sharp increase in the HABs in coastal waters with further warming.
Red tide
Cite
Citations (131)
Cite
Citations (48)
Cycling
Nutrient cycle
Cite
Citations (109)
Abstract Algal blooms have been occurring in Jakarta Bay for twenty years. However, recently the occurrence of algal blooms, their harmful effects, and their duration have been intensified. Algal blooms have devastated the marine environment, caused fish mortality, and been detrimental to local tourism, local fishing, and other industries along the coast. It comes to speculation that the increase of anthropogenic activity from surrounding areas is taking a toll on the environment. So, this research aimed to study the recent rise of algal blooms in Jakarta Bay and the possible anthropogenic links, mainly through cultural eutrophication, to the increasing occurrence of red tides and their impact. Observation has been conducted to study the dynamic of algal blooms concerning eutrophication and the existing seasons. Collecting samples were performed using a canonical plankton net from 2008 until 2015. The results showed that the abundance of phytoplankton ranged from 40.90 x 10 6 up to 1699.10 x 10 6 cells.m −3 . The highest quantity of cells was observed in May 2010 between rainy to dry seasons. There is evidence that the reported increase in frequency and magnitude of algal bloom events in Jakarta Bay is linked to cultural eutrophication. The recent exponential growth of the city may be a contributing factor in the increasing intensity of algal blooms. The cultural eutrophication of coastal waters increased, leading to the intensity and frequency of algal bloom.
Bloom
Red tide
Fish kill
Cite
Citations (6)